Additive manufacturing system and method and feature extraction method

ABSTRACT

An additive manufacturing (AM) method includes using an AM tool to fabricate a plurality of workpiece products; measuring qualities of the first workpiece products respectively; performing a temperature measurement on each of the melt pools on the powder bed; performing photography on each of the melt pools on the powder bed; extracting a length and a width of each of the melt pools; performing a melt-pool feature processing operation; first converting each of the workspace images to a gray level co-occurrence matrix (GLCM); building a conjecture model by using a plurality of sets of first process data and the actual metrology values of the first workpiece products in accordance with a prediction algorithm; and predicting a virtual metrology value of the second workpiece product by using the conjecture model based on a set of second process data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a Divisional Application of the U.S.application Ser. No. 16/854,927, filed Apr. 22, 2020, which is acontinuation-in-part application of U.S. application Ser. No.16/591,613, filed on Oct. 2, 2019, which claims the benefit of theProvisional Application Ser. No. 62/740,435 filed on Oct. 3, 2018; andthe Provisional Application Ser. No. 62/808,865 filed on Feb. 22, 2019.The U.S. application Ser. No. 16/854,927, filed Apr. 22, 2020, claimsthe benefit of the Provisional Application Ser. No. 62/837,211 filed onApr. 23, 2019. The entire disclosures of all the above applications arehereby incorporated by reference herein.

BACKGROUND Field of Invention

The disclosure relates to an additive manufacturing (AM) system, an AMmethod and an AM feature extraction method, and more particularly, to anAM system, an AM method and an AM feature extraction method that supportvirtual metrology (VM).

Description of Related Art

Additive manufacturing (AM), also referred to as 3D-printing, is atechnique of heating metal powders or plastic material to bemelt-shapeable after a digital computing model file is built, and thenfabricating a workpiece by stacking layers. A powder bed fusion processis one of popular additive manufacturing techniques. The powder bedfusion process may be such as a selective laser melting (SLM) process,or a selective laser sintering (SLS) process. The selective lasermelting process is performed by placing powders on a substrate, andusing a high energy laser to irradiate a position at which a powdermolding is desired to be formed, thereby melting and fusing the powders.The selective laser sintering process is also performed by using a laserto irradiate powders to sinter and fuse the powders into blocks, andthen placing another layer of powders thereon to repeat the laserprocess until the product is formed.

A conventional AM tool lacks an online (on production line) tuningmechanism. While fixed values of process parameters (such as laserpower, scan strategy, layer thickness, and scan speed, etc.) are used bythe conventional AM tool for production, the production quality of theAM tool would vary with process variations (such as power distribution,flow control, and moisture content). A conventional AM techniqueperforms quality measurements only after the products are completed forensuring production quality. However, additive manufactured products aremade by processing powders layer by layer, and thus poor processingquality of one certain layer often affect the quality of end product.Therefore, there is a need to provide an AM system, an AM method and anAM feature extraction method for obtaining product quality in time toadjust values of process parameters of an AM tool on a production line.

SUMMARY

An object of the disclosure is to provide an AM method and an AM featureextraction method, thereby obtaining product quality in time such thatvalues of process parameters of an AM tool can be adjusted on aproduction line.

According to the aforementioned object, an aspect of the disclosure isto provide an AM feature extraction method. In the AM feature extractionmethod, a temperature measurement is performed on each of melt poolsformed on each of powder layers stacked on a powder bed during afabrication of a workpiece product, thereby obtaining a temperature ofeach of the melt pools of the workpiece product; photograph is performedon each of the melt pools on the powder bed during the fabrication ofthe workpiece product, thereby obtaining images of the melt pools of theworkpiece product; and photography is performed on each of the powderlayers after the each of the powder layers is placed on the powder bedand before the energy beam is applied to the each of the powder layers,thereby obtaining plural workspace images of the powder layers of eachworkpiece product during the fabrication of the each workpiece product.Then, each of the workspace images is first converted to a gray levelco-occurrence matrix (GLCM) and then a homogeneity index of each powderlayer of each workpiece product is calculated based on the GLCM.Meanwhile, a length and a width of each of the melt pools are extractedfrom the images. Thereafter, a melt-pool feature processing operation isperformed to convert the length, the width and the temperature of eachof the melt pools to a melt-pool length feature, a melt-pool widthfeature and a melt-pool temperature feature of the workpiece product.

In some embodiments, the aforementioned melt-pool length feature,melt-pool width feature and melt-pool temperature feature include amaximum value, a minimum value, a mean value, a variance, a standarddeviation, a skewness of statistic distribution, a kurtosis of statisticdistribution, a full distance and/or a set of quantile of lengths of themelt pools in each of the at least one predetermined area; a maximumvalue, a minimum value, a mean value, a variance, a standard deviation,a skewness of statistic distribution, a kurtosis of statisticdistribution, a full distance and/or a set of quantile of widths of themelt pools in each of the at least one predetermined area; and a maximumvalue, a minimum value, a mean value, a variance, a standard deviation,a skewness of statistic distribution, a kurtosis of statisticdistribution, a full distance and/or a set of quantiles of temperaturesof the melt pools in each of the at least one predetermined area.

In some embodiments, the aforementioned AM feature extraction methodfurther includes extracting a central location of each of the melt poolsfrom the image of each of the melt pools; and performing the melt-poolfeature processing operation to convert the central location of each ofthe melt pools to a central-location feature of the workpiece product.

According to the aforementioned object, another aspect of the disclosureis to provide an AM method. In the AM method, an AM tool is used tofabricate workpiece products, wherein the workpiece products are dividedinto first workpiece products and a second workpiece product, and thesecond workpiece product is fabricated after the first workpieceproducts. An operation of fabricating each of the workpiece productsincludes placing powder layers layer by layer on a powder bed; anddirecting an energy beam to powder bodies on each of the powder layerssequentially after the each of the powder layers is placed on the powderbed to melt powder bodies to form melt pools. Then, qualities of thefirst workpiece products are measured respectively after the firstworkpiece products are completely fabricated, thereby obtaining actualmetrology values of the first workpiece products. A temperaturemeasurement is performed on each of the melt pools on the powder bedduring a fabrication of each of the workpiece products, therebyobtaining a temperature of each of the melt pools of each of theworkpiece products; photography is performed on each of the melt poolson the powder bed during the fabrication of each of the workpieceproducts, thereby obtaining an image of each of the melt pools of eachof the workpiece products; and photography is performed on each of thepowder layers after the each of the powder layers is placed on thepowder bed and before the energy beam is applied to the each of thepowder layers, thereby obtaining a workspace image of each powder layerof each workpiece product during the fabrication of the each workpieceproduct. Then, each of the workspace images is first converted to a graylevel co-occurrence matrix (GLCM) and then a homogeneity index of eachpowder layer of each workpiece product is calculated based on the GLCM.Meanwhile, a length and a width of each of the melt pools are extractedfrom the image of each of the melt pools. Then, a melt-pool featureprocessing operation is performed to convert the length, the width andthe temperature of each of the melt pools to a melt-pool length feature,a melt-pool width feature and a melt-pool temperature feature of each ofthe workpiece products. Then, a conjecture model is built by usingplural sets of first process data and the actual metrology values of thefirst workpiece products in accordance with a prediction algorithm, inwhich the sets of first process data include the melt-pool lengthfeature, the melt-pool width feature and the melt-pool temperaturefeature of each of the first workpiece products. Thereafter, a virtualmetrology value of the second workpiece product is predicted by usingthe conjecture model based on a set of second process data, in which theset of second process data includes the melt-pool length feature, themelt-pool width feature and the melt-pool temperature feature of thesecond workpiece product.

In some embodiments, the AM further includes performing a simulationoperation based on the sets of process data and/or the actual metrologyvalues of the workpiece products, thereby generating a set of suggestedparameter ranges; generating a set of process-parameter adjusted valuesbased on the virtual metrology value; generating a set ofprocess-parameter tracking values based on the set of process-parameteradjusted values, the set of suggested parameter ranges and a set ofparameter design values; and controlling and adjusting the AM tool toprocess the second workpiece product in accordance with the set ofprocess-parameter tracking values.

Thus, with the applications of the embodiments of the disclosure, thequality of an end product or a product that is being processed layer bylayer can be obtained in time, and thus process parameters of an AM toolcan be adjusted on a production line, thereby increasing yield.

It is to be understood that both the foregoing general description andthe following detailed description are by examples, and are intended toprovide further explanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention can be more fully understood by reading the followingdetailed description of the embodiment, with reference made to theaccompanying drawings as follows:

FIG. 1A is a schematic diagram showing an additive manufacturing (AM)system in accordance with some embodiments of the disclosure;

FIG. 1B is a schematic diagram showing an additive manufacturing (AM)tool on a process stage in accordance with some embodiments of thedisclosure;

FIG. 2A is a schematic block diagram of an in-situ metrology system inaccordance with some embodiments of the disclosure;

FIG. 2B is a schematic diagram showing additive manufacturing (AM)features in accordance with some embodiments of the disclosure;

FIG. 2C is a schematic diagram showing extraction results of additivemanufacturing (AM) features in accordance with some embodiments of thedisclosure;

FIG. 3 a schematic block diagram of a virtual metrology (VM) system inaccordance with some embodiments of the disclosure;

FIG. 4 a schematic block diagram of a compensator in accordance withsome embodiments of the disclosure;

FIG. 5A illustrates a schematic flow chart showing an additivemanufacturing (AM) method in accordance with some embodiments of thedisclosure;

FIG. 5B illustrates a schematic flow chart showing a homogeneity indexacquisition method for each workspace image in accordance with someembodiments of the disclosure;

FIG. 5C is a schematic diagram showing converting a workspace image to agray level co-occurrence matrix (GLCM); and

FIG. 5D show homogeneity indexes of powder layers in accordance with anexample of the disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the present embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers are used in thedrawings and the description to refer to the same or like parts.

Referring to FIG. 1A, FIG. 1A is a schematic diagram showing an additivemanufacturing (AM) system 10 in accordance with some embodiments of thedisclosure, in which switches C11, C12 and C13 are used for selecting ifdata are inputted to a corresponding system or device. The AM system 10includes an AM tool 100, a product metrology system 120, an in-situmetrology system 200, a VM system 130, a compensator 140, a trackplanner 150, a controller 160, a simulator 170 and an augmented reality(AR) device 180.

Referring to FIG. 1B, FIG. 1B is a schematic diagram showing the AM tool100 on a process stage in accordance with some embodiments of thedisclosure. The AM tool 100 includes a powder bed 110 and a fusionsystem 102 (such as a laser source). The power bed 110 includes a powderbed container 116 in which a substrate 114 and powder layers 112alternatively stacked on the substrate 114 are held, in which the powderbed 112 includes plural powder bodies. The AM tool 100 uses a powder bedfusion process to fabricate workpiece products. During the fabricationprocess of each of the workpiece products, the fusion system 102 is usedto provide an energy beam 104 to the respective powder bodies of thepowder bed 110, and the powder layers 112 are melted to form and obtaina desired profile of the workpiece product by controlling specificparameters. When the respective powder bodies are melted. Plural meltpools are formed on the powder bed 110. In some embodiments, the powderbed fusion process is a selective laser melting (SLM) process, or aselective laser sintering (SLS) process.

As shown in FIG. 1A, an object of the simulator 170 is to find feasibleprocess parameter ranges, and the AR device 180 is configured to assistthe operation and maintenance of the AM tool 100. The product metrologysystem 120, the in-situ metrology system 200, the VM system 130 areintegrated to estimate variation of each material layer on the powderbed 110 of the AM tool 100. The compensator 140 can compensate processvariation online (on the production line) or offline (off the productionline) by adjusting process parameters. The product metrology system 120is configured to measure qualities of the workpiece productsrespectively after the workpiece products are completely fabricated,thereby obtaining plural actual metrology values EM (such as an actualmetrology value of surface roughness or porosity, etc.). The in-situmetrology system 200 is configured to collect a set of feature data IMof each melt pool on the powder bed 110 and a homogeneity index of eachpowder layer during a fabrication process of each workpiece product, andthe set of feature data IM (melt pool characteristics) includes amelt-pool length feature, a melt-pool width feature and a melt-pooltemperature feature.

The VM system 130 is configured to use sets of process data PD andactual metrology values EM of the workpiece products to predict avirtual metrology value VM (such as a virtual metrology value of surfaceroughness or porosity, etc.) of a next workpiece product processed bythe AM tool 100 in accordance with a prediction algorithm after theworkpiece products have been fabricated by the AM tool 100, each of thesets of process data PD including the melt-pool length feature, themelt-pool width feature and the melt-pool temperature feature of each ofthe workpiece products. In addition, the sets of process data PD alsomay include process parameter data PP (such as laser power values, etc.)provided by the controller 160 and sensing data IS (such as flow speed,oxygen density, etc.) provided by the AM tool 100.

In some embodiments, the prediction algorithm used by the VM system 130may be a neural network algorithm or a multiple regression algorithm.However, another algorithm is also applicable to the disclosure, such asa back propagation neural network (BPNN) algorithm, a general regressionneural network (GRNN) algorithm, a radial basis function neural network(RBFNN) algorithm, a simple recurrent network (SRN) algorithm, a supportvector data description (SVDD) algorithm, a support vector machine (SVM)algorithm, a multiple regression (MR) algorithm, a partial least squares(PLS) algorithm, a nonlinear iterative partial least Squares (NIPALS)algorithm, or a generalized linear models (GLMs), etc. Thus, thedisclosure is not limited thereto.

The simulator 170 is configured to perform a simulation operation basedon the sets of process data PD and/or the actual metrology values EM ofthe workpiece products, thereby generating a set of suggested parameterranges PR. The compensator 140 is configured to generate a set ofprocess-parameter adjusted values based on the virtual metrology valueVM of the next workpiece product, in which the process-parameteradjusted values may be divided into on-line (on the production line)process-parameter adjusted values PA_(on) and off-line process-parameteradjusted values PA_(off). The track planner 150 is configured togenerate a set of process-parameter tracking values PT based on the setof off-line process-parameter adjusted values PA_(off), the set ofsuggested parameter ranges PR and a set of parameter design values. Thecontroller 160 is configured to control and adjust the AM tool 100 toprocess the next workpiece product in accordance with the set ofprocess-parameter tracking values PT. The simulator 170 used in theembodiments of the disclosure may be based on U.S. Patent PublicationNo. 20190128738, which is hereby incorporated by reference.

Hereinafter, the in-situ metrology system 200 is explained. Referring toFIG. 2A, FIG. 2A is a schematic block diagram of the in-situ metrologysystem 200 in accordance with some embodiments of the disclosure. Thein-situ metrology system 200 includes a coaxial camera 202, a pyrometer204, an overview camera 206 and an in-situ metrology server 210. Thepyrometer 204 is configured to perform a temperature measurement on eachof the melt pools on the powder bed during a fabrication of each of theworkpiece products, thereby obtaining a temperature of each of the meltpools of each of the workpiece products. In some embodiments, thepyrometer 204 is configured to perform a temperature measurement on eachmelt pool of each powder layer on the powder bed during a fabrication ofeach of the workpiece products. The coaxial camera 202 is configured toperform photography on each of the melt pools on the powder bed duringthe fabrication of each of the workpiece products, thereby obtaining animage of each of the melt pools of each of the workpiece products. Theoverview camera 206 is configured to perform photography on the powderbed layer by layer, thereby obtaining a workspace image of each layer ofeach workpiece product during the fabrication of the each workpieceproduct. The in-situ metrology server 210 includes an image-featureextraction device 220, a multithread allocation device 230, a FTP (FileTransfer Protocol) client 240, melt-pool feature processing devices232/242 and a workspace image processing device 248. The image-featureextraction device 220 is configured to extract a length and a width ofeach of the melt pools from the image of each of the melt pools, andextract a melt-pool temperature of each melt pool from the melt-pooltemperatures measured by the pyrometer 204. The melt-pool featureprocessing devices 232/242 are configured to convert the length, thewidth and the temperature of each melt pool of each workpiece product toa melt-pool length feature, a melt-pool width feature and a melt-pooltemperature feature of each workpiece product. The workspace imageprocessing device 248 is configured to convert each of the workspaceimages to a gray level co-occurrence matrix (GLCM), thereby obtaining ahomogeneity index as the base for homogeneity evaluation. A method forobtaining the homogeneity index will be described later. After amelt-pool length feature, a melt-pool width feature and a melt-pooltemperature feature of each workpiece product, and a homogeneity indexof each layer of each workpiece product are obtained, the VM system usesplural sets of process data and the actual metrology values of theworkpiece products to predict a virtual metrology value of a nextworkpiece product processed by the AM tool in accordance with aprediction algorithm after the workpiece products have been fabricatedby the AM tool, the sets of process data including the melt-pool lengthfeature, the melt-pool width feature and the melt-pool temperaturefeature of each of the workpiece products, and a homogeneity index ofeach layer of each workpiece product.

There are two computing loading modes in the in-situ metrology system200, which are a light loading mode and a heavy loading mode. The lightloading mode is applicable to workpieces with simple structures, such asthe workpieces with no or few supporting pieces. The heavy loading modeis applicable to workpieces with complicated structures, such as theworkpieces with a lot of supporting pieces, and the workpieces withdiversified geometrical shapes.

The light loading mode and the heavy loading mode depend on thephotographing frequency of the coaxial camera 202 and the sampling rateof the image-feature extraction device 220. A user may select a switchC21 or C22 to activate the light loading mode or the heavy loading modein accordance with actual requirements. In the light loading mode, thefeatures are extracted by conventional image preprocessing, and incontrast, the heavy loading mode uses a CNN (Conventional NeuralNetwork)-based method in parallel computation. In the light loadingmode, due to the high sampling rate, the in-situ metrology system 200uses the multithread allocation device 230 to distribute a large amountof melt-pool images to different cores in a computer. In the heavyloading mode, the in-situ metrology system 200 is built on a parallelprocessing platform 246 (such as Hadoop). Hadoop is a distributedparallel processing platform for big data, which can start melt poolfeature extraction (MPFE) per requests. A CNN-based MPFE can identifywidths, lengths, and central locations of melt pools in differentisothermal envelopes.

An additive manufacturing (AM) feature extraction method performed bythe in-situ metrology system 200 according to some embodiments of thedisclosure will be described in the below. Referring to FIG. 2B and FIG.2C, FIG. 2A is a schematic block diagram of an in-situ metrology systemin accordance with some embodiments of the disclosure; and FIG. 2B is aschematic diagram showing additive manufacturing (AM) features inaccordance with some embodiments of the disclosure.

At first, during the powder bed fusion process of a workpiece product,the coaxial camera 202 is used at a predetermined frequency (forexample, 4 kHz) to perform photograph on the powder bed, so as to obtainn melt-pool images (such as a melt-pool image 260 shown in FIG. 2B), themelt-pool images including an image of each melt pool (such as a meltpool Al shown in FIG. 2B). Meanwhile, the pyrometer 204 is used at apredetermined frequency (for example, 100 kHz) to perform temperaturemeasurements on the powder bed, thereby obtaining the temperature ofeach melt pool, and the overview camera 206 is used at a predeterminedfrequency (for example, 4 kHz) to perform photography on the powder bedlayer by layer after each layer is placed on the powder bed and beforean energy beam is applied to the layer, thereby obtaining pluralworkspace images. The workspace images can be used subsequently to findthe locations at which abnormal quality occurs, and thus can be used asthe base for quality evaluation, such as homogeneity evaluation.

Thereafter, the image-feature extraction device 220 receives the imageand temperature of each melt pool, and the images of work space images.The image-feature extraction device 220 stores these data into a memory222, and provides instant download through the FTP server 224. Then, themelt-pool feature processing devices 232 or 242 processes the above dataat a sample rate (for example 25 images/second), thereby selecting msample images and their corresponding temperatures T_(i) from themelt-pool images, where i=1 to m, m>0. Thereafter, the melt-pool featureprocessing devices 232 or 242 extracts a length Li, a width W_(i), and acentral location (X_(i), Y_(i)) of each melt pool from the m samplesimages, in which X and Y are values of coordinates (such as an image 262shown in FIG. 2B), so as to obtain sample melt-pool data FFi=(W_(i), Li,X_(i), Y_(i), T_(i)), such as shown in FIG. 2C. Then, the melt-poolfeature processing devices 232 or 242 performs a melt-pool featureprocessing operation to convert the sample melt-pool data FFi=(W_(i),Li, X_(i), Y_(i), T_(i)) to a melt-pool length feature, a melt-poolwidth feature and a melt-pool temperature feature of each melt pool ofthe workpiece product. In the melt-pool feature processing operation, atfirst, at least one predetermined area S_(j) is defined at a peripheryof the central location (X_(i), Y_(i)), for example, an area extending±3 pixels from the central location (X_(i), Y_(i)). In some embodiments,the predetermined area S_(j) is an area containing all of the samplemelt-pool data FFi=(W_(i), Li, X_(i), Y_(i), T_(i)). Then, the melt-poollength feature L_(j), the melt-pool width feature W_(j), and themelt-pool temperature feature T_(j) in the predetermined area S_(j) iscalculated. The melt-pool length feature L_(j) includes a maximum value,a minimum value, a mean value, a variance, a standard deviation, askewness of statistic distribution, a kurtosis of statisticdistribution, a full distance and/or a set of quantile of lengths of themelt pools in the predetermined area S_(j). The melt-pool width featureW_(j) includes a maximum value, a minimum value, a mean value, avariance, a standard deviation, a skewness of statistic distribution, akurtosis of statistic distribution, a full distance and/or a set ofquantile of widths of the melt pools in each of the ne predeterminedarea S_(j). The melt-pool temperature feature T_(j) includes a maximumvalue, a minimum value, a mean value, a variance, a standard deviation,a skewness of statistic distribution, a kurtosis of statisticdistribution, a full distance and/or a set of quantile of temperaturesof the melt pools in each of the ne predetermined area S_(j). It isunderstood that the computation methods of the maximum value, theminimum value, the mean value, the variance, the standard deviation, theskewness of statistic distribution, the kurtosis of statisticdistribution, the full distance and the set of quantile of data points(such as lengths, widths or temperatures) are well known by those havingordinary knowledge in the art, and thus are not described herein.Meanwhile, the workspace image processing device 248 obtains andconverts each of the workspace images to a gray level co-occurrencematrix (GLCM), thereby obtaining a homogeneity index. Hereinafter, theVM system 130 is described. Referring to FIG. 3, FIG. 3 a schematicblock diagram of the VM system 130 in accordance with some embodimentsof the disclosure. The VM system 130 used in embodiments of thedisclosure can be referenced to U.S. Pat. No. 8,095,484 B2, andembodiments of the disclosure can be combined with a VM system based onU.S. Pat. No. 8,095,484 B2, which is hereby incorporated by reference.

The VM system 130 is divided into a model-building stage and aconjecturing stage. In the model-building stage, the VM system 130builds a conjecture model by using plural sets of historical processdata PD obtained when plural historical workpiece products arefabricated, and actual metrology values EM of the historical workpieceproducts measured after complete fabrication in accordance with aprediction algorithm. The VM system 130 also builds a process dataquality index (DQI_(x)) model and a global similarity index (GSI) modeby using the sets of historical process data PD of the historicalworkpiece products, and computes a DQI_(x) threshold and a GSIthreshold. The VM system 130 also builds a metrology data quality index(DQI_(y)) model by using the actual metrology values EM of thehistorical workpiece products, and computes a DQI_(y) threshold. The RIvalue is designed to gauge the reliance level of a virtual metrologyvalue. The GSI value is used to assess the degree of similarity betweenthe current set of input process data and all of the sets of processdata used for building and training a conjecture model. The GSI value isprovided to help the RI value gauge the reliance level of the VM system130. The DQI_(x) value is used to evaluate whether a set of process dataused for producing a workpiece is abnormal, and the DQI_(y) value isused to evaluate whether the metrology data of the workpiece areabnormal.

In the conjecturing stage, the VM system 130 predicts a virtualmetrology value VM of a workpiece product to be measured by using theconjecture model based on a set of process data PD that is obtained whenthe workpiece product to be measured is fabricated by the AM tool 100.The sets of process data and historical process data PD include themelt-pool length feature, the melt-pool width feature and the melt-pooltemperature feature of each of the historical workpiece product and theworkpiece product to be measured, and the homogeneity index of eachlayer of each of the historical workpiece product and the workpieceproduct to be measured. Besides, the process data and historical processdata PD may also include process parameter data PP (such as laser powervalues, etc.) provided by the controller 160 and sensing data IS (suchas flow speed, oxygen density, etc.) provided by the AM tool 100. It isnoted that the VM system 130 may conjecture a VM value of an end-productworkpiece or VM values of respective material layers of one productworkpiece.

The compensator 140 will be described in the below. Referring to FIG. 4,FIG. 4 a schematic block diagram of the compensator 140 in accordancewith some embodiments of the disclosure. The process parameters (such aslaser power, scan speed, etc.) of the AM machine 100 can be adjusted bythe compensator 140 with virtual metrology values VM based on anevolution optimization method. The compensator 140 includes a parameteroptimization device 142 and a fuzzy controller 144. The parameteroptimization device 142 selects optimal parameters with, for example, aHybrid Taguchi-Genetic Algorithm (HTGA) and quality objective parameterranges. The fuzzy controller 144 suggests the on-line process-parameteradjusted values PA_(on) through scan rules. Then, the controller 160modifies its parameters by layers according to the on-lineprocess-parameter adjusted values PA_(on).

Hereinafter, an additive manufacturing (AM) method is explained.Referring to FIG. 5A, FIG. 5A illustrates a schematic flow chart showingan AM method in accordance with some embodiments of the disclosure. Asshown in FIG. 5, step 310 is first performed to use an AM tool tofabricate workpiece products, in which the workpiece products includesfirst workpiece products (i.e. the historical workpiece products formolding building) and a second workpiece product (the workpiece productto be measured), and the second workpiece product is fabricated afterthe first workpiece products. An operation of fabricating each of theworkpiece products includes placing powder layers layer by layer on apowder bed; and after each of the powder layers is placed on the powderbed, directing an energy beam to powder bodies on the each of the powderlayers sequentially to melt powder bodies to form melt pools. Qualitiesof the first workpiece products are measured respectively after thefirst workpiece products are completely fabricated (step 320), therebyobtaining actual metrology values of the first workpiece products. Atemperature measurement is performed on each of the melt pools on thepowder bed during a fabrication of each of the workpiece products,thereby obtaining a temperature of each of the melt pools of each of theworkpiece products (step 330). Photograph is performed on each of themelt pools on the powder bed during the fabrication of each of theworkpiece products (step 340), thereby obtaining an image of each of themelt pools of each of the workpiece products. Photograph is performed oneach of the powder layers on the powder bed during the fabrication ofeach of the workpiece products (step 342), thereby obtaining a workspaceimage of each of the powder layers of each of the workpiece products. Alength, a width and a central location of each of the melt pools isextracted from the image of each of the melt pools (step 350).Thereafter, a melt-pool feature processing operation and homogeneityindex acquisition (step 360) are performed to convert the length, thewidth and the temperature of each of the melt pools to a melt-poollength feature, a melt-pool width feature and a melt-pool temperaturefeature of each of the workpiece products, and to convert a workspaceimage of each powder layer of each workpiece product to a homogeneityindex. Thereafter, a conjecture model is built by using plural sets offirst process data and the actual metrology values of the firstworkpiece products in accordance with a prediction algorithm (step 370),in which the sets of first process data include the homogeneity index ofeach powder layer of each of the workpiece products, the melt-poollength feature, the melt-pool width feature and the melt-pooltemperature feature of each of the first workpiece products. Then, avirtual metrology value of the second workpiece product (the product tobe measured) is predicted by using the conjecture model based on a setof second process data (step 380), in which the set of second processdata includes the melt-pool length feature, the melt-pool width featureand the melt-pool temperature feature of the second workpiece product.

Hereinafter, a method for converting a workspace image of each powderlayer to a homogeneity index is explained. Referring to FIG. 5B, FIG. 5Billustrates a schematic flow chart showing a homogeneity indexacquisition method for each workspace image in accordance with someembodiments of the disclosure. In the homogeneity index acquisitionmethod, a workspace image of a powder layer is obtained (step 362) aftera laser process has been performed on the powder layer. Then, workspaceimage calibration step 364 is performed to calibrate the position andthe view angle of the workspace image, and to define a range ofworkspace on the workspace image. Thereafter, a workspace imagesegmentation step 366 is performed to divide the workspace image into nby n pixel regions (for example 5×5), such as a greylevel intensityimage 410 shown in FIG. 5C. Thereafter, a homogeneity index calculationstep 368 is performed to obtain a homogeneity index from the greylevelintensity image 410.

In the homogeneity index calculation step 368, the greylevel intensityimage 410 is first converted to a gray level co-occurrence matrix (GLCM)420. As shown in FIG. 5C, For the greylevel intensity image 410 in whicheach pixel region has a respective intensity, the GLCM 420 for thegreylevel intensity image 410 indicates how often a pixel region of acertain intensity occurs next to a pixel region of another certainintensity. In the example shown in FIG. 5C, the GLCM 420 derived fromthe greylevel intensity image 410 records how many times a pixel regionof a first intensity (the intensity varying from 0 to 7) occursimmediately to the right of a pixel region of a second intensity, wherethe first intensity is shown on the horizontal axis (columns) and thesecond intensity is shown on the vertical axis of the GLCM 420 (rows).As shown in the greylevel intensity image 410, there one occurrence of apixel region of intensity 5 occurring immediately to the right of apixel region of intensity 4, such as “1” shown in the 5^(th) column ofthe 4^(th) row of the GLCM 620. In the greylevel intensity image 410,there are two occurrences of a pixel region of intensity 0 occurringimmediately to the right of a pixel of intensity 1, such as “2” shown inthe 0^(th) column of the 1^(st) row of the GLCM 620. However, there areno occurrences in greylevel intensity image 410 of a pixel region ofintensity 2 occurring immediately to the right of a pixel region ofintensity 1, as shown in the 1^(th) column of the 2^(nd) row of the GLCM620. Then, a homogeneity index of the image 610 is calculated based onthe GLCM 620 according to the following equation.

$\begin{matrix}{{{Homogeneity}{Index}} = {\sum_{i}{\sum_{j}\frac{P_{ij}}{\lbrack {1 + ( {i - j} )^{2}} \rbrack}}}} & (1)\end{matrix}$

where i, j are intensities, which are integers from 0 to 7; and

-   -   P_(i, j) are values in the GLCM.

For additive manufacturing, the powder layers must be deposited withconstant thickness and homogeneity. The homogeneity index represents thedegree of homogeneity of one powder layer. The homogeneity index may beused to determine if the powder layer is uniform. Referring to FIG. 5D,FIG. 5D shows homogeneity indexes of powder layers in accordance with anexample of the disclosure. As shown in FIG. 5D, the homogeneity indexesof the first 18 powder layers are small, and thus the first 18 powderlayers are relatively not uniform. The homogeneity indexes of powderlayers above the 18 ^(th) powder layer are large, and thus those powderlayers are quite uniform. It is noted that embodiment of the disclosureuse homogeneity indexes of powder layers to predict a virtual metrologyvalue of a workpiece product.

It is understood that the aforementioned steps described in theembodiments of the disclosure can be combined or skipped, and the orderthereof can adjusted according actual requirements. The aforementionedembodiments can be realized as a computer program product, which mayinclude a machine-readable medium on which instructions are stored forprogramming a computer (or other electronic devices) to perform aprocess based on the embodiments of the present invention. Themachine-readable medium can be, but is not limited to, a floppydiskette, an optical disk, a compact disk-read-only memory (CD-ROM), amagneto-optical disk, a read-only memory (ROM), a random access memory(RAM), an erasable programmable read-only memory (EPROM), anelectrically erasable programmable read-only memory (EEPROM), a magneticor optical card, a flash memory, or another type ofmedia/machine-readable medium suitable for storing electronicinstructions. Moreover, the embodiments of the present invention alsocan be downloaded as a computer program product, which may betransferred from a remote computer to a requesting computer by usingdata signals via a communication link (such as a network connection orthe like).

It can be known from the aforementioned embodiments that, by using theAM system provided by the embodiments of the disclosure, the AM tool canbe effectively controlled in time. By using the AM feature extractionmethod provided by the embodiments of the disclosure, AM features can beeffectively extracted form an enormous amount of data, therebysuccessfully performing virtual metrology on additive manufacturedproducts, thus obtaining the quality of an end product or an productthat are being processed layer by layer in time, such that processparameters of an AM tool can be adjusted on a production line forincreasing yield.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the structure of thedisclosure without departing from the scope or spirit of the invention.In view of the foregoing, it is intended that the disclosure covermodifications and variations of this invention provided they fall withinthe scope of the following claims.

What is claimed is:
 1. An additive manufacturing (AM) method,comprising: using an AM tool to fabricate a plurality of workpieceproducts, wherein the workpiece products are divided a plurality offirst workpiece products and a second workpiece product, and the secondworkpiece product is fabricated after the first workpiece products, anoperation of fabricating each of the workpiece products comprising:placing a plurality of powder layers layer by layer on a powder bed; andafter each of the powder layers is placed on the powder bed, directingan energy beam to a plurality of powder bodies on the each of the powderlayers sequentially to melt powder bodies to form a plurality of meltpools; measuring qualities of the first workpiece products respectivelyafter the first workpiece products are completely fabricated, therebyobtaining a plurality of actual metrology values of the first workpieceproducts; performing a temperature measurement on each of the melt poolson the powder bed during a fabrication of each of the workpieceproducts, thereby obtaining a temperature of each of the melt pools ofeach of the workpiece products; performing photography on each of themelt pools on the powder bed during the fabrication of each of theworkpiece products, thereby obtaining an image of each of the melt poolsof each of the workpiece products; performing photography on each of thepowder layers after the each of the powder layers is placed on thepowder bed and before the energy beam is applied to the each of thepowder layers, thereby obtaining a plurality of workspace images of thepowder layers of each workpiece product during the fabrication of theeach workpiece product; extracting a length and a width of each of themelt pools from the image of each of the melt pools; performing amelt-pool feature processing operation to convert the length, the widthand the temperature of each of the melt pools to a melt-pool lengthfeature, a melt-pool width feature and a melt-pool temperature featureof each of the workpiece products; first converting each of theworkspace images to a gray level co-occurrence matrix (GLCM) and thencalculate a homogeneity index of each powder layer of each workpieceproduct based on the GLCM; building a conjecture model by using aplurality of sets of first process data and the actual metrology valuesof the first workpiece products in accordance with a predictionalgorithm, the sets of first process data comprising the homogeneityindex of each powder layer of each of the workpiece products, themelt-pool length feature, the melt-pool width feature and the melt-pooltemperature feature of each of the first workpiece products; andpredicting a virtual metrology value of the second workpiece product byusing the conjecture model based on a set of second process data, theset of second process data comprising the melt-pool length feature, themelt-pool width feature and the melt-pool temperature feature of thesecond workpiece product.
 2. The additive manufacturing (AM) method ofclaim 1, further comprising: performing a simulation operation based onthe sets of process data and/or the actual metrology values of theworkpiece products, thereby generating a set of suggested parameterranges; generating a set of process-parameter adjusted values based onthe virtual metrology value; generating a set of process-parametertracking values based on the set of process-parameter adjusted values,the set of suggested parameter ranges and a set of parameter designvalues; and controlling and adjusting the AM tool to process the secondworkpiece product in accordance with the set of process-parametertracking values.
 3. The additive manufacturing (AM) method of claim 1,wherein the melt-pool length feature, the melt-pool width feature andthe melt-pool temperature feature comprise a maximum value, a minimumvalue, a mean value, a variance, a standard deviation, a skewness ofstatistic distribution, a kurtosis of statistic distribution, a fulldistance and/or a set of quantile of lengths of the melt pools in eachof the at least one predetermined area; a maximum value, a minimumvalue, a mean value, a variance, a standard deviation, a skewness ofstatistic distribution, a kurtosis of statistic distribution, a fulldistance and/or a set of quantile of widths of the melt pools in each ofthe at least one predetermined area; and a maximum value, a minimumvalue, a mean value, a variance, a standard deviation, a skewness ofstatistic distribution, a kurtosis of statistic distribution, a fulldistance and/or a set of quantiles of temperatures of the melt pools ineach of the at least one predetermined area.
 4. The additivemanufacturing (AM) method of claim 1, further comprising: extracting acentral location of each of the melt pools from the image of each of themelt pools; and performing the melt-pool feature processing operation toconvert the central location of each of the melt pools to acentral-location feature of each of the workpiece products.
 5. Anadditive manufacturing (AM) feature extraction method, comprising:performing a temperature measurement on each of a plurality of meltpools formed on each of a plurality of powder layers stacked on a powderbed during a fabrication of a workpiece product, thereby obtaining atemperature of each of the melt pools of the workpiece product;performing photography on each of the melt pools on the powder bedduring the fabrication of the workpiece product, thereby obtaining aplurality of images of the melt pools of the workpiece product;performing photography on each of the powder layers after the each ofthe powder layers is placed on the powder bed and before the energy beamis applied to the each of the powder layers, thereby obtaining aplurality of workspace images of the powder layers of each workpieceproduct during the fabrication of the each workpiece product; firstconverting each of the workspace images to a gray level co-occurrencematrix (GLCM) and then calculate a homogeneity index of each powderlayer of each workpiece product based on the GLCM; extracting a lengthand a width of each of the melt pools from the images; and performing amelt-pool feature processing operation to convert the length, the widthand the temperature of each of the melt pools to a melt-pool lengthfeature, a melt-pool width feature and a melt-pool temperature featureof the workpiece product.
 6. The additive manufacturing (AM) featureextraction method of claim 5, wherein the melt-pool length feature, themelt-pool width feature and the melt-pool temperature feature comprise amaximum value, a minimum value, a mean value, a variance, a standarddeviation, a skewness of statistic distribution, a kurtosis of statisticdistribution, a full distance and/or a set of quantile of lengths of themelt pools in each of the at least one predetermined area; a maximumvalue, a minimum value, a mean value, a variance, a standard deviation,a skewness of statistic distribution, a kurtosis of statisticdistribution, a full distance and/or a set of quantile of widths of themelt pools in each of the at least one predetermined area; and a maximumvalue, a minimum value, a mean value, a variance, a standard deviation,a skewness of statistic distribution, a kurtosis of statisticdistribution, a full distance and/or a set of quantiles of temperaturesof the melt pools in each of the at least one predetermined area.
 7. Theadditive manufacturing (AM) feature extraction method of claim 5,further comprising: extracting a central location of each of the meltpools from the image of each of the melt pools; and performing themelt-pool feature processing operation to convert the central locationof each of the melt pools to a central-location feature of the workpieceproduct.